Abstract
This paper presents a new Memetic Algorithm designed to compute near optimal solutions for the MinLA problem. It incorporates a highly specialized crossover operator, a fast MinLA heuristic used to create the initial population and a local search operator based on a fine tuned Simulated Annealing algorithm. Its performance is investigated through extensive experimentation over well known benchmarks and compared with other state-of-the-art algorithms.
This work is supported by the CONACyT Mexico, the “Contrat Plan Etat Région” project COM (2000-2006) as well as the Franco-Mexican Joint Lab in Computer Science LAFMI (2005-2006).
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Rodriguez-Tello, E., Hao, JK., Torres-Jimenez, J. (2006). Memetic Algorithms for the MinLA Problem. In: Talbi, EG., Liardet, P., Collet, P., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2005. Lecture Notes in Computer Science, vol 3871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11740698_7
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DOI: https://doi.org/10.1007/11740698_7
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